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Modeling and Querying Metadata in the
Semantic Sensor Web: stRDF and stSPARQL
Manolis Koubarakis
Kostis Kyzirakos
Manos Karpathiotakis
Dept. of Informatics and Telecommunications
National and Kapodistrian University of Athens
Dagstuhl SSN 26/01/2010

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Talk Outline
• Motivation
• The Data Model stRDF
• The Query Language stSPARQL
• Implementation
• Future Work
Dagstuhl SSN 26/01/2010

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Motivation
• The vision of the Semantic Sensor Web: annotate sensor
data and services to enable discovery, integration,
interoperability etc. (Sheth et al. 2008, SemsorGrid4Env)
• Sensor annotations involve thematic, spatial and temporal
metadata. Examples:
– The sensor measures temperature. (thematic)
– The sensor is located in the location represented by point
(A, B). (spatial)
– The sensor measured −30
Celsius on 26/01/2010 at
03:00pm. (temporal)
• RDF can represent thematic metadata. How about spatial and
temporal metadata?
Dagstuhl SSN 26/01/2010

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Previous Work
• Time in RDF (Gutierrez and colleagues, 2005).
• SPARQL-ST (Perry, 2008).
• SPAUK (Kollas, 2007)
Dagstuhl SSN 26/01/2010

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Our Approach
• Use ideas from constraint databases (Kanellakis, Kuper and
Revesz, 1991). Slogan: What’s in a tuple? Constraints.
• Extend RDF to a constraint database model. Slogan: What’s
in a triple? Constraints.
• Extend SPARQL to a constraint query language.
• Follow exactly the approach of CSQL (Kuper et al., 1998).
• Use linear constraints to represent spatial and temporal
metadata.
Dagstuhl SSN 26/01/2010

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Spatial Metadata
• We start with a FO language L = {≤, +} ∪ Q over the structure
Q = Q, ≤, +, (q)q∈Q .
• Atomic formulae: linear equations and inequalities of the form
(
p
i=1
aixi)Θa0
where Θ is one of =, ≤ or <.
• Semi-linear point sets: sets that can be defined by quantifier-free
formulas of L.
Dagstuhl SSN 26/01/2010

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Example
(−x + 1.363636y < −5.272576 ∧ x − 0.090909y < 131.818133 ∧ y <
79 ∧ −y < −13) ∨ (y < 13 ∧ x < 133 ∧ −x − 1.5y < −128.5)
Dagstuhl SSN 26/01/2010

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The sRDF data model
• Let I, B and L be the sets of IRIs, blank nodes and literals.
• Let Ck be the set of quantifier-free formulae of L with k free
variables (k = 1, 2, . . .).
• Let C be the infinite union C1 ∪ C2 ∪ · · ·.
• An sRDF triple is an element of the set
(I ∪ B) × I × (I ∪ B ∪ L ∪ C).
• An sRDF graph is a set of sRDF triples.
• sRDF can be realized as an extension of RDF with a new kind
of typed literals: quantifier-free formulae with linear
constraints. The datatype of these literals is
strdf:SemiLinearPointSet.
Dagstuhl SSN 26/01/2010

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Example of sRDF graph
ex:s1 rdf:type, ex:Sensor .
ex:s1 ex:has_location "x=40 and y=15"^^strdf:SemiLinearPointSet .
Dagstuhl SSN 26/01/2010

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Introducing Time
• Time structure: the set of rational numbers Q (i.e., time is
assumed to be linear, dense and unbounded).
• Temporal constraints are expressed by quantifier-free formulas
of the language L defined earlier, but their syntax is limited to
elements of the set C1.
• Atomic temporal constraints: formulas of L of the
following form: x ∼ c, where x is a variable, c is a rational
number and ∼ is <, ≤, ≥, >, = or =.
• Temporal constraints: Boolean combinations of atomic
temporal constraints using a single variable.
Dagstuhl SSN 26/01/2010

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Example
1 5 10 12 15
t = 1 ∨ (t ≥ 5 ∧ t ≤ 10) ∨ (t ≥ 12 ∧ t ≤ 15)
Dagstuhl SSN 26/01/2010

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The stRDF data model
stRDF extends sRDF with the ability to represent the valid time
of a triple:
• An stRDF quad (a, b, c, τ) is an sRDF triple (a, b, c) with a
fourth component τ which is a temporal constraint.
• An stRDF graph is a set of sRDF triples and stRDF quads.
Dagstuhl SSN 26/01/2010

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Example of stRDF graph
ex:obs1Result om:uom ex:Celcius .
ex:obs1Result om:value "27"
"(10 <= t <= 11)"^^strdf:SemiLinearPointSet .
Dagstuhl SSN 26/01/2010

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The Query Language stSPARQL
• Syntactic extension of SPARQL
• Formal semantics
• Closure
• Computability
• Low data complexity
• Efficient implementation
Dagstuhl SSN 26/01/2010

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Example - Dataset I
Sensor metadata using the SSN ontology:
ex:sensor1 rdf:type ssn:Sensor .
ex:sensor1 ssn:measures ex:temperature .
ex:temperature rdf:type ssn:PhysicalQuality .
ex:sensor1 ssn:supports ex:grounding1 .
ex:grounding1 rdf:type ssn:SensorGrounding .
ex:grounding1 ssn:hasLocation ex:location1 .
ex:location1 rdf:type ssn:Location .
ex:location1 strdf:hasGeometry
"x=40 and y=15"^^strdf:SemiLinearPointSet .
ex:sensor2 rdf:type ssn:Sensor .
Dagstuhl SSN 26/01/2010

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Queries - Dataset I
Spatial selection. Find the URIs of the sensors that are inside the
rectangle R(0, 0, 100, 100)?
select ?S
where { ?S rdf:type ssn:Sensor .
?G rdf:type ssn:SensorGrounding .
?L rdf:type ssn:Location .
?S ssn:supports ?G .
?G ssn:haslocation ?L .
?L strdf:hasGeometry ?GEO .
filter(?GEO inside "0<=x<=100 and 0<=y<=100") }
Dagstuhl SSN 26/01/2010

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Answer
?S
ex:sensor1
Dagstuhl SSN 26/01/2010

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Queries - Dataset I
Spatial selection with OPTIONAL. Find the URIs of the sensors that
optionally has a grounding that has a location which is inside the rectangle
R(0, 0, 100, 100)?
select ?S ?GEO
where { ?S rdf:type ssn:Sensor .
optional {
?G rdf:type ssn:SensorGrounding .
?L rdf:type ssn:Location .
?S ssn:supports ?G .
?G ssn:haslocation ?L .
?L strdf:hasGeometry ?GEO .
filter(?GEO inside "0<=x<=100 and 0<=y<=100") } }
Dagstuhl SSN 26/01/2010

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Answer
?S ?GEO
ex:sensor1 "x=40 and y=15"^^strdf:SemiLinearPointSet
ex:sensor2
Dagstuhl SSN 26/01/2010

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Example - Dataset II
Sensor observation metadata using the O&M ontology:
ex:sensor1 rdf:type ex:TemperatureSensor .
ex:TemperatureSensor rdfs:subClassOf om:Sensor .
ex:obs1 rdf:type om:Observation .
ex:obs1 om:procedure ex:sensor1 .
ex:obs1 om:observedProperty ex:temperature .
ex:temperature rdf:type om:Property .
ex:obs1 om:observationLocation ex:obslocation1 .
ex:obslocation1 rdf:type om:Location .
ex:obslocation1 strdf:hasGeometry
"x=40 and y=15"^^strdf:SemiLinearPointSet .
ex:obs11 om:result ex:obs1Result .
ex:obs1Result rdf:type om:ResultData .
ex:obs1Result om:uom ex:Celcius .
ex:obs1Result om:value "27"
"(10 <= t <= 11)"^^strdf:SemiLinearPointSet .
Dagstuhl SSN 26/01/2010

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Example - Dataset II (cont’d)
Metadata about geographical areas:
ex:area1 rdf:type ex:RuralArea .
ex:area1 ex:hasName "Brovallen" .
ex:area1 strdf:hasGeometry
"(-x+1.363636y<-5.272576 and y<79 and -y<-13 and
x-0.090909y<131.818133) or (y<13 and x<133 and
-x-1.5y<-128.5)"^^strdf:SemiLinearPointSet .
Dagstuhl SSN 26/01/2010

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Queries - Dataset II
Spatial and temporal selection. Find the values of all observations that
were valid at time 11 and the rural area they refer to.
select ?V ?RA
where { ?OBS rdf:type om:Observation .
?LOC rdf:type om:Location .
?R rdf:type om:ResultData .
?RA rdf:type ex:RuralArea .
?OBS om:observationLocation ?LOC .
?LOC strdf:hasGeometry ?OBSLOC .
?OBS om:result ?R .
?R om:value ?V ?T .
?RA strdf:hasGeometry ?RAGEO .
filter(?T contains "t = 11" && ?RAGEO contains ?OBSLOC) }
Dagstuhl SSN 26/01/2010

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Answer
?V ?RA
"27" ex:area1
Dagstuhl SSN 26/01/2010

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Example - Dataset III
Moving sensor metadata using the SSN ontology:
ex:sensor2 rdf:type ssn:Sensor .
ex:sensor2 ssn:measures ex:temperature .
ex:sensor2 ssn:supports ex:grounding2 .
ex:grounding2 rdf:type ssn:SensorGrounding .
ex:grounding2 ssn:hasLocation ex:location2 .
ex:location2 rdf:type ssn:Location .
Dagstuhl SSN 26/01/2010

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Example - Dataset III (cont’d)
ex:location2 strdf:hasTrajectory
"(x=10t and y=5t and 0<=t<=5) or
(y=5t and x=50 and 25<=y<=50)"^^strdf:SemiLinearPointSet.
Dagstuhl SSN 26/01/2010

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Example - Dataset III (cont’d)
Metadata about geographical areas:
ex:area1 rdf:type ex:RuralArea .
ex:area1 ex:hasName "Brovallen" .
ex:area1 strdf:hasGeometry
"(-x+1.363636y<-5.272576 and y<79 and -y<-13 and
x-0.090909y<131.818133) or (y<13 and x<133 and
-x-1.5y<-128.5)"^^strdf:SemiLinearPointSet .
Dagstuhl SSN 26/01/2010

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Queries - Dataset III
Intersection of an area with a trajectory. Which areas of Brovallen
were sensed by a moving sensor and when?
select (?TR[1,2] INTER ?GEO) as ?SENSEDAREA ?GEO[3]
where { ?SN rdf:type ssn:Sensor .
?Y rdf:type ssn:Location .
?X rdf:type ssn:SensorGrounding .
?RA rdf:type ex:RuralArea .
?SN ssn:supports ?X .
?X ssn:hasLocation ?Y .
?Y strdf:hasTrajectory ?TR .
?RA ex:hasName "Brovallen" .
?RA strdf:hasGeometry ?GEO .
filter(?TR[1,2] overlap ?GEO) }
Dagstuhl SSN 26/01/2010

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Answer
?SENSEDAREA ?T1
"((y=0.5x and 0<=x<=50) or
(x=50 and 25<=y<=50)) and
((y<79 and -y<-13 and
-x+1.363636y<-5.272576 and
x-0.090909y<131.818133) or
(y<13 and x<133 and
-x-1.5y<-128.5))"
^^strdf:SemiLinearPointSet
"0 <= t <= 10"
^^strdf:SemiLinearPointSet.
Dagstuhl SSN 26/01/2010

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One More Query
Projection and spatial function application. Find the URIs of the
sensors that are north of Brovallen.
select ?SN
where { ?SN rdf:type ssn:Sensor .
?X rdf:type ssn:SensorGrounding .
?RA rdf:type ex:RuralArea .
?SN ssn:supports ?X .
?X ssn:hasLocation ?Y .
?Y strdf:hasGeometry ?SN_LOC .
?RA ex:hasName "Brovallen".
?RA strdf:hasGeometry ?GEO .
filter(MAX(?GEO[2])<MIN(?SN_LOC[2])) }
Dagstuhl SSN 26/01/2010

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Answer
?SN
Dagstuhl SSN 26/01/2010

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What Else is There in stSPARQL Syntax?
• k-ary spatial terms
“(x ≥ 1 ∧ x = y) ∨ y = 7”
?GEO ∩ “(x ≥ 1 ∧ x = y) ∨ y = 7”
BD(?GEO ∩ “(x ≥ 1 ∧ x = y) ∨ y = 7”)
“(x ≥ 1 ∧ x ≤ 10 ∧ y ≥ 0 ∧ x = z)”[1, 2] ∩ “(z ≥ 0 ∧ z ≤ 10)”
• Metric spatial terms
AREA(“(x ≥ 1 ∧ x ≤ 10 ∧ y ≥ 0 ∧ x = y)”)
MIN(“(x ≥ 1 ∧ x ≤ 10 ∧ y ≥ 0 ∧ x = y)”[1])
• Spatial and temporal selection predicates
Dagstuhl SSN 26/01/2010

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stSPARQL Semantics
• Extension of the SPARQL semantics of (Perez et al., 2006).
– Extend the concept of mapping.
– The semantics of AND, OPT, UNION remain the same.
– We need to define carefully the evaluation of spatial terms
and the semantics of spatial and temporal filters.
Dagstuhl SSN 26/01/2010

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The System Strabon
PostGIS
Query Engine
Parser
Optimizer
Evaluator
Transaction
Manager
Storage Manager
Repository
SAIL
RDBMS
Convex
Partitioner
Constraint Engine
Constraint
Solver
Representation
Translator
Dagstuhl SSN 26/01/2010

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Implementation Details: Store
DNF
Constraints
(at most 2 vars)
PostGIS
Vectorspace
cddliborbital
Dagstuhl SSN 26/01/2010

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Implementation Details: Query
Parser Optimizer
Query
Processor
stSPARQL
query
results
PostGIS
Vector to
Constraints
Dagstuhl SSN 26/01/2010

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Strabon in SemsorGrid4Env
PostGIS
Query
Engine
Storage
Manager
Constraint
EngineMetadata provider
Metadata consumer
Strabon
Service
Registration
Query
Semantic Registration
and Discovery Service
Dagstuhl SSN 26/01/2010

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Future Work
• Complete the theory (complexity results).
• Complete the implementation (see demo for what works now).
• Integrate with the rest of SemsorGrid4Env architecture
components.
• Incomplete information (use the blank nodes for a bit more
than what they are currently used).
• OWL 2 and constraints?
Dagstuhl SSN 26/01/2010